Due to the inherent properties of a dynamical system, the amount of fluorescent dye in the field of view is highly time-dependent. This fact results in a flickering motion in the original footage. Histogram matching has been applied for uniform white balance throughout the images and to suppress these flickering events. A Gaussian blur was then applied to despeckle the images before applying a binary mask to disregard the background and enhance the contrast of the image series. An example of this mask is depicted in Fig. (REF ), where the original in vivo geometry can be found along with the corresponding mask, created by averaging all images and tracing the resulting mean image. Particle tracking has been carried out via a custom-tailored Matlab®(MathWorks, Massachusetts, USA) script (see also [1]}, [2]}). By this technique, we were able to extract the position data of the moving cells. We stress that due to abundant breathing movements, not all images of a series can be analyzed but rather split into subseries of images where no displacements of the vessels are visible. However, alterations in tissue thickness may lead to deficiencies in image quality. The same holds for the fact that vessels are winding in a three-dimensional topology, and thus, the focal plane will only capture a certain part of the geometry. Therefore, a complete automatized analysis is complicated and possible in only some peculiar cases. For most analyses, manual adjustments and evaluations must be carried out.
Since the plasma in the hamsters is stained with a fluorescent dye, the apparent RBCs obey lower brightness values than cell-free (plasma rich) areas. From the corresponding temporal brightness distributions, one can therefore define voids and the passing RBCs. For uniform characterization of void durations, we binarized the signal with respect to the mean value, i.e., all the signals above the mean will be considered as void. In each branch, the mean passage time of RBCs are determined and denoted by \(\tau _\text{RBC}\) . For a better comparison within a geometry, we seek to achieve a normalized void duration. Therefore, we have divided the calculated void durations by \(\tau _\text{RBC}\)  in the respective branch to achieve a normalization by the flow rate. These normalized void durations are then sorted in ascending order to obtain the empirical cumulative distribution function. We postulate this empirical cumulative distribution function to be represented by a log-normal distribution function \(\text{cdf}\left(\frac{\tau _n}{\tau _\text{RBC}}\right)\) ,

cdf(nRBC)=12[1+erf((nRBC)-2)] ,

with the error function \(\text{erf}(\cdot )\) , and parameters \(\hat{\mu },\hat{\sigma }\in \mathbb {R},\hat{\sigma }>0\) .
These paramaters are estimated based on our dataset using the maximum-likelihood approach, yielding

 = 1Nn=1N(nRBC) ,
